Summary of Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels, by Emma Svensson et al.
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels
by Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter, Lewis Mervin, Adam Arany, Ola Engkvist
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to improve the accuracy of machine learning models used in early drug discovery stages by quantifying uncertainty in predictions. They focus on leveraging censored labels, which provide thresholds rather than precise values, to enhance model performance and trust. The authors adapt various ensemble-based, Bayesian, and Gaussian models using the Tobit model from survival analysis to learn from these censored labels. Their results show that incorporating censored labels can lead to more accurate and reliable modeling in real-world pharmaceutical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how scientists use computers to help find new medicines. They want to make sure their predictions are correct, so they’re trying to figure out how to deal with uncertainty in their models. One way they do this is by using something called censored labels, which give them clues about certain values without telling them exactly what those values are. The authors of the paper use special tools to learn from these censored labels and improve their models’ accuracy. By doing so, they hope to make it easier for scientists to find new medicines that work well. |
Keywords
» Artificial intelligence » Machine learning
